Amenities

Golf Courses

Code

source("_common.R")

Warning: package 'sf' was built under R version 4.3.3

Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE

terra 1.7.55

Attaching package: 'dplyr'

The following objects are masked from 'package:terra':

    intersect, union

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Loading required package: viridisLite

Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
remotes::install_github('r-tmap/tmap')

Attaching package: 'tidyr'

The following object is masked from 'package:terra':

    extract

Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)

Rows: 1751442 Columns: 104

── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (66): compositepropertylinkagekey, originalapn, onlineformattedparcelid,...
dbl (31): clip, fips, fipscode, apnparcelnumberunformatted, apnsequencenumbe...
lgl  (7): previousclip, taxaccountnumber, mobilehomeindicator, effectiveyear...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)

Code

golf=read_sf("/Users/ashleylowe/Trails/golf_courses.shp/golf_courses.shp")
golf=st_transform(golf, 4326 )
golf=golf[c(1,7,8,12)]


tmap_mode("view")

tmap mode set to interactive viewing

Code

center_lon <- -157.45
center_lat <- 20.75
zoom_level <- 7

tm_shape(golf) +
  tm_dots(
    col = "Name",
    palette = "magma",
    size = 0.1,
    id = "Name",
    popup.vars = c("Name" = "Name"),
    legend.show = FALSE      # hides the legend
  ) +
  tm_basemap() +
  tm_view(set.view = c(center_lon, center_lat, zoom_level))

Warning: Number of levels of the variable "Name" is 80, which is larger than
max.categories (which is 30), so levels are combined. Set
tmap_options(max.categories = 80) in the layer function to show all levels.

Distance to each Property calculation

Code

# 1. find nearest feature index
nearest_idx <- st_nearest_feature(PB_HawaiiInfo, golf)

# 2. get nearest trails (same row order as PB_HawaiiInfo)
nearest_trails <- golf[nearest_idx, ]

# 3. calculate distance (element-wise)
nearest_dist_m <- st_distance(PB_HawaiiInfo, nearest_trails, by_element = TRUE)

# 4. merge trail attributes + distance into PB_HawaiiInfo
PB_HawaiiInfo <- PB_HawaiiInfo %>%
  mutate(
    nearest_golf = as.numeric(nearest_dist_m)
  ) %>%
  bind_cols(st_drop_geometry(nearest_trails))  # adds trail attributes

Nature Reserves

Centralized location of managed lands protecting natural and cultural resources of the Main Hawaiian Islands and Kure Atoll.

We will use this one to create a buffer and identify the properties that adjoin a reserve.

Code

reserves <- read_sf("/Users/ashleylowe/Trails/Reserves/Reserves.shp")
reserves$managedby <- gsub("[()]", "", reserves$managedby)

tmap_mode("view")

center_lon <- -157.45
center_lat <- 20.75
zoom_level <- 7

tm_shape(reserves) +
  tm_polygons(
    "managedby",
    palette = "magma",
    id = "name",
    popup.vars = c("Managed By" = "managedby"),
    legend.show = FALSE      # hides the legend
  ) +
  tm_basemap() +
  tm_view(set.view = c(center_lon, center_lat, zoom_level))

To adjoin we will focus on matching with buffer of the reserve location and use the tax plot match to adjoin to the property data. Add a buffer distance of 10 meters and then match with the Hawaii tmz file.

Code

reserves <- st_transform(reserves, 32605)
reserves_buffered <- st_buffer(reserves, dist = 10)
reserves_buffered <- st_transform(reserves_buffered, 4326)
intersections <- st_intersects(HI_2023_shapefile, reserves_buffered)
HI_intersections <- HI_2023_shapefile[lengths(intersections) > 0, ]



ggplot() +
    geom_sf(data = HI_intersections, fill = "brown", color = "grey") +
    geom_sf(data = reserves_buffered, fill = "forestgreen", color = "black") +
  geom_sf(data = coastline, color = "black") +
    coord_sf(
        xlim = c(-158.5, -157.5),  # longitudes
        ylim = c(21, 21.9),        # latitudes in correct order
        expand = FALSE
    ) +
    theme_minimal() +
    labs(title = "Buffered Reserves (Green) and Adjoining Properties (Brown)")

The adjoining part will join shapefile to properties with Name of reserve, who manages it and the type of reserve it is.

Code

# Create a vector of indices for the first overlapping shp2 feature per shp1 row
HI_2023_shapefile$reserve_names <- sapply(intersections, function(idx) {
  if (length(idx) == 0) {
    return(NA_character_)
  } else {
    paste(reserves_buffered$name[idx], collapse = ", ")
  }
})

HI_2023_shapefile$managedby <- sapply(intersections, function(idx) {
  if (length(idx) == 0) {
    return(NA_character_)
  } else {
    paste(reserves_buffered$managedby[idx], collapse = ", ")
  }
})

HI_2023_shapefile$type_defin <- sapply(intersections, function(idx) {
  if (length(idx) == 0) {
    return(NA_character_)
  } else {
    paste(reserves_buffered$type_defin[idx], collapse = ", ")
  }
})